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Author Kuo, Tzu-Ming ♦ Lee, Ching-Pei ♦ Lin, Chih-Jen
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Large-scale Kernel Ranksvm ♦ Online Advertisement ♦ Important Task ♦ Web Search ♦ Challenging Issue ♦ Loss Function ♦ Training Kernel Ranksvm ♦ General Optimization Method ♦ Implementation Issue ♦ Lengthy Training Time ♦ State-of-the-art Method ♦ Efficient Method ♦ Recommendation System ♦ Training Instance ♦ Kernel Ranksvm ♦ Pairwise Loss
Abstract Learning to rank is an important task for recommendation systems, online advertisement and web search. Among those learning to rank methods, rankSVM is a widely used model. Both linear and nonlinear (kernel) rankSVM have been extensively studied, but the lengthy training time of kernel rankSVM remains a challenging issue. In this paper, after discussing difficulties of training kernel rankSVM, we propose an efficient method to handle these problems. The idea is to reduce the number of variables from quadratic to linear with respect to the number of training instances, and efficiently evaluate the pairwise losses. Our setting is applicable to a variety of loss functions. Further, general optimization methods can be easily applied to solve the reformulated problem. Implementation issues are also carefully considered. Experiments show that our method is faster than state-of-the-art methods for training kernel rankSVM.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study